personalization
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Meet retail’s new sustainability strategy: Personalization
We have been raised to believe in recycling, but it has mostly been a sham — only 9% of all plastic waste produced in 2018 was recycled. The beauty industry produces over 120 billion units of packaging every year, little of which is recycled. Globally, an estimated 92 million tons of textile waste ends up in landfills.
Reducing waste is key to meeting environmental milestones, and some retail firms have narrowed in on a unique approach to minimize what their customers throw away: personalization. Accurate personalization can guide consumers to the right products, reducing waste while increasing conversion and loyalty.
Reducing waste is key to meeting environmental milestones, and some retail firms have narrowed in on a unique approach to minimize what their customers throw away: personalization.
For big brands and retailers, personalization is expected to be the top category for tech investment this year. Moreover, personalization holds high appeal, with 80% of survey respondents indicating they are more likely to do business with a company if it offers personalized experiences and 90% indicating that they find personalization appealing, according to a survey by Epsilon.
Startups that deliver sustainable personalization solutions that also improve business for retailers and brands fall into three categories:
- AR virtual try-on with shade matching.
- Advanced virtual fitting rooms with VR/AR for fashion.
- Smart packaging with IoT and distributed ledger technology.
AR virtual try-on with shade matching
Faces are easy to map, since it’s not difficult to virtually place a lipstick color on a face, but using AR and AI to recommend skin-tone-matching makeup products has been challenging for many AR virtual try-on companies. “I’ve been searching for an intuitive foundation-shade-finder tool since launching Cult Beauty in 2008, and nothing has lived up to the experience of having a professional match you in daylight until I discovered MIME,” says Alexia Inge, founder of Cult Beauty. “There are so many variables like light, skin tones, prevalent undertones, device, screen, OS, formula density, formula oxidation, as well as preferences for coverage levels, finish, brand and skin type,” she says.
MIME founder and CEO Christopher Merkle said, “Virtual try-on has exploded in the past few years, but for color cosmetics, the technology doesn’t help solve the primary customer pain point: shade matching. From day one, I decided to focus our company’s R&D efforts exclusively on color accuracy. I want to make sure that when the consumer receives their foundation or concealer in the mail, it’s the perfect shade once applied to their skin.”
MIME’s Shade Finder AI allows consumers to take a photo of themselves, answer a few questions, then get matched with a makeup color that pairs with their skin tone. MIME helps retailers and brands increase their online and in-store purchase conversion by up to five times. More than 22% of beauty returns are due to poor customer color purchases, but Merkle says MIME can get returns as low as 0.1%.
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Bluecore lands $125M Series E on $1B valuation as e-commerce personalization grows
During the pandemic, especially when we were in lockdown, just about every retailer had to build its online presence and do it quickly. As people move to shop online in larger numbers, being able to personalize that experience has become more crucial. That made the pandemic a pivotal moment for Bluecore, an e-commerce personalization platform, and today the company announced a $125 million Series E on a $1 billion valuation.
Existing investor Georgian led the round, with participation from other existing investors FirstMark and Norwest, along with new investor Silver Lake Waterman. Today’s investment brings the total raised to $225 million, according to the company.
Until fairly recently, Bluecore CEO and co-founder Fayez Mohamood says that retail outreach was mostly about driving traffic to brick and mortar stores or to the company website, but as more business gets conducted online, it has changed how brands have to interact with their customers.
“We believe in that shift, and Bluecore is a retail-specific, multichannel personalization platform, and we combine basically three types of data. First is customer identity. Second is shopper behavior. And then thirdly and most importantly, the product catalog of a retailer, and using that we drive personalized experiences on various channels,” Mohamood explained.
The company was founded in 2013, and has been able to evolve the notion of personalization since then in a significant way. Mohamood says the pandemic really pushed things into the digital realm where his company’s strength lies, and that’s one of the primary reasons they are taking on this funding.
“Personalization has always been important, but I think the value retailers can derive from it has dramatically accelerated as digital became a bigger and bigger portion of everybody’s revenue stream. And over the last year, that became even more critical,” he said.
As the company’s growth has accelerated, so has the hiring. In May 2020, Bluecore had 236 employees; today it has more than 300, and it’s shooting to be over 400 by the end of the year. He says that as he grows the company, diversity and inclusion is a crucial component to have the employee base reflect the diversity of the customers they serve.
“It starts with the executive team, so I’m extremely proud of the fact that on our executive team close to half our team is female. We have a committee that is represented by the core employees that is a diversity, equity and inclusion committee where we have thoughts and ideas and most most importantly actions on how we can build a better diverse, inclusive workplace. And that translates it into OKRs,” he said.
As a Series E company with a billion-dollar valuation, Mohamood can see becoming a public company at some point, but it is not an immediate goal, as he pursues growth over profitability. “The way we think about it is we have this brand that’s going to help us invest in our product capabilities, our leadership capabilities and our go-to-market capabilities to build something that has the ability to [be a public company some day]. Having said that, we’re pursuing growth, and if that’s the goal, we find that staying private helps us do that,” he said. And with $125 million of runway, the company has plenty of freedom to take its time.
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Spotify rolls out new personalized experiences and playlists, including a mid-year review and a blended mix with a friend
Spotify today is expanding its investment in personalization features with the launch of a dedicated in-app experience called Only You, which focuses on your favorite music and how you listen. The experience is similar to Spotify’s popular annual review, Spotify Wrapped, as it highlights the artists, songs, genres and other aspects of your music listening experience that are important to you, which can then be shared across social media, just as Wrapped is. The company is also today debuting Blend, a new way to create a personalized playlist with a friend.
The Only You hub will live alongside the existing Made for You hub on the Search page inside the Spotify app. In Made for You, you’ll find your other personalized playlists like Discover Weekly, Release Radar, Daily Mixes and others, like Your Time Capsule or Summer Rewind, for example, as well as the more recently added trio of playlist sets, Spotify Mixes.
From now through the end of the month, Only You will be a separate hub in the Spotify app, but it will ultimately be relocated to live inside the Made for You hub.
Image Credits: Spotify
The new Only You experience, meanwhile, will help you discover new trends beyond what you might see in your personalized playlists. This includes “Your Audio Birth Chart,” where the sun is the top artist you listened to over the last six months, rising is your most recent discovery and the moon is an artist you listen to that shows your emotional side; “Your Dream Dinner Party,” where you pick three favorite artists for a custom, frequently updated Spotify Mix featuring favorite songs and fresh picks; and “Your Artist Pairs,” which features unique pairings you’ve listened to recently, like those spanning genres.
It also will contain other personalized insights like the different time periods of music you’ve enjoyed, the music or podcasts you listen to at what time of day and your favorite music genres and podcast topics.
For example, your “Song Year” will show how you’ve traveled through different periods of time, based on the tracks you listened to throughout the year. The first year that will pop up here is the year you’ve streamed the most, while the second year that appears will represent the earlier release year that you’ve listened to. The third year is the most recent song year that’s been streamed.
To gather all this data, Only You looks at your Spotify in-app listening experience over the last six months (December 2020 – May 2021). Users must have streamed 30 tracks across five different artists over the past six months in order to be eligible for the new experience. Spotify says the data isn’t being used for ad targeting purposes. (And despite astrology’s connection to birth months and years, the “Your Audio Birth Chart” isn’t asking for users’ birth year to create this experience.)
Image Credits: Spotify
Another key part of the Only You campaign is the launch of Blend, currently in beta.
This feature will sit on the “Made for Two” shelf within the Only You hub, allowing you to invite any other Spotify user to create a playlist with you. Using similar mixing technology that powers Spotify’s Family Mix and Duo Mix in their respective plans, Blend lets you invite any other Spotify user (free user or paid subscriber) to merge their musical tastes with yours to create a curated playlist featuring songs you both like.
This playlist is updated daily and will grow with users over time as their listening habits change, Spotify says.
Because it works with free accounts, Blend could encourage more users to try Spotify so they can create a playlist with a significant other, best friend, family member or others, even if they’re not on a shared plan.
Image Credits: Spotify
Both the Only You experience and Blend build on technology Spotify had already developed to power other features, like Wrapped and various multi-user blended mixes, rather than creating something entirely new. But the bigger message Spotify wants to convey here is that it’s far ahead of competitors when it comes to personalization features. Even if rivals are duping its playlists, it wants to be the forerunner when it comes to personalized music.
Of course, that’s not always the case. The newer Spotify Mixes, for instance, were a lot like a feature Pandora had launched years prior, which created custom playlists across a number of attributes, including genre and mood. But where Spotify succeeds is its continual release of new personalization features, as it works to make its app customized to the end user. By doing so, the switching costs increase — that is, users will find it harder to jump to rival services due to how many custom playlists they may have on hand.
Spotify will begin heavily marketing the launch of Only You with a number of top artists by creating sets of stats for various fandoms, including those for Harry Styles, Selena Gomez, Lil Nas X, Doja Cat, Justin Bieber, SZA and others. The campaign will run through June 30.
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Breinify announces $11M seed to bring data science to the marketing team
Breinify is a startup working to apply data science to personalization, and do it in a way that makes it accessible to nontechnical marketing employees to build more meaningful customer experiences. Today the company announced a funding round totaling $11 million.
The investment was led by Gutbrain Ventures and PBJ Capital with participation from Streamlined Ventures, CXO Fund, Amino Capital, Startup Capital Ventures and Sterling Road.
Breinify co-founder and CEO Diane Keng says that she and co-founder and CTO Philipp Meisen started the company to bring predictive personalization based on data science to marketers with the goal of helping them improve a customer’s experience by personalizing messages tailored to individual tastes.
“We’re big believers that the world, especially consumer brands, really need strong predictive personalization. But when you think about consumer big brands or the retailers that you buy from, most of them aren’t data scientists, nor do they really know how to activate [machine learning] at scale,” Keng told TechCrunch.
She says that she wanted to make this type of technology more accessible by hiding the complexity behind the algorithms powering the platform. “Instead of telling you how powerful the algorithms are, we show you [what that means for the] consumer experience, and in the end what that means for both the consumer and you as a marketer individually,” she said.
That involves the kind of customizations you might expect around website messaging, emails, texts or whatever channel a marketer might be using to communicate with the buyer. “So the AI decides you should be shown these products, this offer, this specific promotion at this time, [whether it’s] the web, email or SMS. So you’re not getting the same content across different channels, and we do all that automatically for you, and that’s [driven by the algorithms],” she said.
Breinify launched in 2016 and participated in the TechCrunch Disrupt Startup Battlefield competition in San Francisco that year. She said it was early days for the company, but it helped them focus their approach. “I think it gave us a huge stage presence. It gave us a chance to test out the idea just to see where the market was in regards to needing a solution like this. We definitely learned a lot. I think it showed us that people were interested in personalization,” she said. And although the company didn’t win the competition, it ended up walking away with a funding deal.
Today the startup is growing fast and has 24 employees, up from 10 last year. Keng, who is an Asian woman, places a high premium on diversity.
“We partner with about four different kinds of diversity groups right now to source candidates, but at the end of the day, I think if you are someone that’s eager to learn, and you might not have all the skills yet, and you’re [part of an under-represented] group we encourage everyone to apply as much as possible. We put a lot of work into trying to create a really well-rounded group,” she said.
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Hightouch raises $2.1M to help businesses get more value from their data warehouses
Hightouch, a SaaS service that helps businesses sync their customer data across sales and marketing tools, is coming out of stealth and announcing a $2.1 million seed round. The round was led by Afore Capital and Slack Fund, with a number of angel investors also participating.
At its core, Hightouch, which participated in Y Combinator’s Summer 2019 batch, aims to solve the customer data integration problems that many businesses today face.
During their time at Segment, Hightouch co-founders Tejas Manohar and Josh Curl witnessed the rise of data warehouses like Snowflake, Google’s BigQuery and Amazon Redshift — that’s where a lot of Segment data ends up, after all. As businesses adopt data warehouses, they now have a central repository for all of their customer data. Typically, though, this information is then only used for analytics purposes. Together with former Bessemer Ventures investor Kashish Gupta, the team decided to see how they could innovate on top of this trend and help businesses activate all of this information.
HighTouch co-founders Kashish Gupta, Josh Curl and Tejas Manohar.
“What we found is that, with all the customer data inside of the data warehouse, it doesn’t make sense for it to just be used for analytics purposes — it also makes sense for these operational purposes like serving different business teams with the data they need to run things like marketing campaigns — or in product personalization,” Manohar told me. “That’s the angle that we’ve taken with Hightouch. It stems from us seeing the explosive growth of the data warehouse space, both in terms of technology advancements as well as like accessibility and adoption. […] Our goal is to be seen as the company that makes the warehouse not just for analytics but for these operational use cases.”
It helps that all of the big data warehousing platforms have standardized on SQL as their query language — and because the warehousing services have already solved the problem of ingesting all of this data, Hightouch doesn’t have to worry about this part of the tech stack either. And as Curl added, Snowflake and its competitors never quite went beyond serving the analytics use case either.
Image Credits: Hightouch
As for the product itself, Hightouch lets users create SQL queries and then send that data to different destinations — maybe a CRM system like Salesforce or a marketing platform like Marketo — after transforming it to the format that the destination platform expects.
Expert users can write their own SQL queries for this, but the team also built a graphical interface to help non-developers create their own queries. The core audience, though, is data teams — and they, too, will likely see value in the graphical user interface because it will speed up their workflows as well. “We want to empower the business user to access whatever models and aggregation the data user has done in the warehouse,” Gupta explained.
The company is agnostic to how and where its users want to operationalize their data, but the most common use cases right now focus on B2C companies, where marketing teams often use the data, as well as sales teams at B2B companies.
Image Credits: Hightouch
“It feels like there’s an emerging category here of tooling that’s being built on top of a data warehouse natively, rather than being a standard SaaS tool where it is its own data store and then you manage a secondary data store,” Curl said. “We have a class of things here that connect to a data warehouse and make use of that data for operational purposes. There’s no industry term for that yet, but we really believe that that’s the future of where data engineering is going. It’s about building off this centralized platform like Snowflake, BigQuery and things like that.”
“Warehouse-native,” Manohar suggested as a potential name here. We’ll see if it sticks.
Hightouch originally raised its round after its participation in the Y Combinator demo day but decided not to disclose it until it felt like it had found the right product/market fit. Current customers include the likes of Retool, Proof, Stream and Abacus, in addition to a number of significantly larger companies the team isn’t able to name publicly.
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Boost ROI with intent data and personalized multichannel marketing campaigns
Coronavirus is causing large and small businesses to drastically cut marketing budgets. In Forrester’s self-described “most optimistic scenario,” the analysts project a 28% drop in U.S. marketing spend by the end of 2021. Even Google is cutting its marketing budget in half. As marketers move forward, Forrester predicts marketing automation platforms will grow despite an overall decline in marketing technology investment.
Automation platforms help marketers scale their communications. However, scaling communications is not a substitute for intimacy, which all humans crave. Because of the pandemic, it is harder than ever to get attention, let alone make a connection. More mass email blasts from your marketing automation platform are not going to get you the connections with prospects you crave. So how should marketers proceed? Direct mail captures 100% of your audience’s attention. It provides a sensory experience for your prospects and customers, and that helps establish an emotional connection.
Winning marketers are strategically merging automation and digital data with the more intimate channel of direct mail. We call this tactile marketing automation (TMA).
TMA is the integration of direct mail or personalized swag with a marketing automation platform. With TMA, a marketer doesn’t have to think about creating direct mail campaigns outside of digital campaigns. Rather, direct mail experiences are already fully integrated into the pre-built customer journey.
TMA uses intent data to inform content, messaging and the timing of direct mail touchpoints that maximize relevancy and scalability. Multichannel campaigns including direct mail report an ROI 18 percentage points higher than those without direct mail. Plus, 84% of marketers state direct mail improves multichannel campaign performance.
Read on to see how you can merge digital communications and direct mail to deliver remarkable experiences that spark a connection.
Incorporate intent data
Personalization is a key ingredient of a remarkable experience. Many marketers automate processes by introducing marketing software and then call it personalization. But, oftentimes it’s just quicker batching and blasting. Brands can’t just change the first name on a piece of content and call it “personalized.” Real personalization is necessary and vital for real results. Our consumers expect more. The best way to introduce real personalization within a marketing mix is to use intent data and trigger-driven campaigns.
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Five ways to bring a UX lens to your AI project
Debbie Pope
Contributor
Contributor
Debbie Pope (she/her) is senior manager of product at The Trevor Project, the world’s largest suicide prevention and crisis intervention organization for LGBTQ youth. A 2019 Google AI Impact Grantee, the project is building an AI system to identify and prioritize high-risk contacts while simultaneously supporting more youth.
As AI and machine-learning tools become more pervasive and accessible, product and engineering teams across all types of organizations are developing innovative, AI-powered products and features. AI is particularly well-suited for pattern recognition, prediction and forecasting, and the personalization of user experience, all of which are common in organizations that deal with data.
A precursor to applying AI is data — lots and lots of it! Large data sets are generally required to train an AI model, and any organization that has large data sets will no doubt face challenges that AI can help solve. Alternatively, data collection may be “phase one” of AI product development if data sets don’t yet exist.
Whatever data sets you’re planning to use, it’s highly likely that people were involved in either the capture of that data or will be engaging with your AI feature in some way. Principles for UX design and data visualization should be an early consideration at data capture, and/or in the presentation of data to users.
1. Consider the user experience early
Understanding how users will engage with your AI product at the start of model development can help to put useful guardrails on your AI project and ensure the team is focused on a shared end goal.
If we take the ‘”Recommended for You” section of a movie streaming service, for example, outlining what the user will see in this feature before kicking off data analysis will allow the team to focus only on model outputs that will add value. So if your user research determined the movie title, image, actors and length will be valuable information for the user to see in the recommendation, the engineering team would have important context when deciding which data sets should train the model. Actor and movie length data seem key to ensuring recommendations are accurate.
The user experience can be broken down into three parts:
- Before — What is the user trying to achieve? How does the user arrive at this experience? Where do they go? What should they expect?
- During — What should they see to orient themselves? Is it clear what to do next? How are they guided through errors?
- After — Did the user achieve their goal? Is there a clear “end” to the experience? What are the follow-up steps (if any)?
Knowing what a user should see before, during and after interacting with your model will ensure the engineering team is training the AI model on accurate data from the start, as well as providing an output that is most useful to users.
2. Be transparent about how you’re using data
Will your users know what is happening to the data you’re collecting from them, and why you need it? Would your users need to read pages of your T&Cs to get a hint? Think about adding the rationale into the product itself. A simple “this data will allow us to recommend better content” could remove friction points from the user experience, and add a layer of transparency to the experience.
When users reach out for support from a counselor at The Trevor Project, we make it clear that the information we ask for before connecting them with a counselor will be used to give them better support.
Image Credits: Trevor Project (opens in a new window)
If your model presents outputs to users, go a step further and explain how your model came to its conclusion. Google’s “Why this ad?” option gives you insight into what drives the search results you see. It also lets you disable ad personalization completely, allowing the user to control how their personal information is used. Explaining how your model works or its level of accuracy can increase trust in your user base, and empower users to decide on their own terms whether to engage with the result. Low accuracy levels could also be used as a prompt to collect additional insights from users to improve your model.
3. Collect user insights on how your model performs
Prompting users to give feedback on their experience allows the Product team to make ongoing improvements to the user experience over time. When thinking about feedback collection, consider how the AI engineering team could benefit from ongoing user feedback, too. Sometimes humans can spot obvious errors that AI wouldn’t, and your user base is made up exclusively of humans!
One example of user feedback collection in action is when Google identifies an email as dangerous, but allows the user to use their own logic to flag the email as “Safe.” This ongoing, manual user correction allows the model to continuously learn what dangerous messaging looks like over time.
Image Credits: Google
If your user base also has the contextual knowledge to explain why the AI is incorrect, this context could be crucial to improving the model. If a user notices an anomaly in the results returned by the AI, think of how you could include a way for the user to easily report the anomaly. What question(s) could you ask a user to garner key insights for the engineering team, and to provide useful signals to improve the model? Engineering teams and UX designers can work together during model development to plan for feedback collection early on and set the model up for ongoing iterative improvement.
4. Evaluate accessibility when collecting user data
Accessibility issues result in skewed data collection, and AI that is trained on exclusionary data sets can create AI bias. For instance, facial recognition algorithms that were trained on a data set consisting mostly of white male faces will perform poorly for anyone who is not white or male. For organizations like The Trevor Project that directly support LGBTQ youth, including considerations for sexual orientation and gender identity are extremely important. Looking for inclusive data sets externally is just as important as ensuring the data you bring to the table, or intend to collect, is inclusive.
When collecting user data, consider the platform your users will leverage to interact with your AI, and how you could make it more accessible. If your platform requires payment, does not meet accessibility guidelines or has a particularly cumbersome user experience, you will receive fewer signals from those who cannot afford the subscription, have accessibility needs or are less tech-savvy.
Every product leader and AI engineer has the ability to ensure marginalized and underrepresented groups in society can access the products they’re building. Understanding who you are unconsciously excluding from your data set is the first step in building more inclusive AI products.
5. Consider how you will measure fairness at the start of model development
Fairness goes hand-in-hand with ensuring your training data is inclusive. Measuring fairness in a model requires you to understand how your model may be less fair in certain use cases. For models using people data, looking at how the model performs across different demographics can be a good start. However, if your data set does not include demographic information, this type of fairness analysis could be impossible.
When designing your model, think about how the output could be skewed by your data, or how it could underserve certain people. Ensure the data sets you use to train, and the data you’re collecting from users, are rich enough to measure fairness. Consider how you will monitor fairness as part of regular model maintenance. Set a fairness threshold, and create a plan for how you would adjust or retrain the model if it becomes less fair over time.
As a new or seasoned technology worker developing AI-powered tools, it’s never too early or too late to consider how your tools are perceived by and impact your users. AI technology has the potential to reach millions of users at scale and can be applied in high-stakes use cases. Considering the user experience holistically — including how the AI output will impact people — is not only best-practice but can be an ethical necessity.
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Spotify rolls out a more personalized home screen to users worldwide
Spotify has been slowly rolling out a redesigned mobile app in small sections — first with an update to podcast pages, then to other parts of the experience. Today, the company is revamping the most critical part of the Spotify app: the home screen. Now, when Spotify users launch the app, they’ll notice the new home screen greets them depending on what time of day it is with a “Good Morning,” “Good Afternoon” or “Good Evening,” for example. But the screen’s content and recommendations will also change with the time of day, Spotify says, and the content has also been better organized so you more easily jump back in or browse recommendations from the main page.
Before, Spotify’s home screen emphasized your listening history by putting at the top of the page things like your “Recently Played,” “Your Top Podcasts” and “Your Heavy Rotation.”
Effectively, the update separates the app’s home screen into two main parts: familiar content on top and new or recommended content on the bottom half.
Now, the home screen reserves six spots underneath the daily greeting where you can continue with things like the podcast you stream every morning, your workout playlist or the album you’ve been listening to on heavy rotation this week. This content will update as your day progresses to better match your activities and interests, based on prior behavior.
Beneath these six spots, the home page will display other things like your top podcasts, “made for you” playlists, recommendations for new discoveries based on your listening and more.
The concept for the new home screen is similar to what Pandora recently rolled out with its personalized “For You” tab late last year. Like Spotify, Pandora’s tab also customizes the content displayed based on the time of day, in addition to the day of the week and other predictions it can make about a customer’s mood or potential activity, based on prior listening data.
Pandora’s revamp led to double the number of users engaging with the personalized page, compared with the old Browse experience, it says. Spotify, too, is likely hoping to see a similar bump in usage and engagement, as users won’t have to dart around the app as much to find their favorite content or recommendations. That way, they’ll be able to start streaming more quickly after the app is launched, potentially leading to longer sessions and more discovery of new content.
Spotify to date has defined itself by its advanced personalization and recommendation technology, but its app hasn’t always been the easiest to use and navigate — especially in comparison to its top U.S. rival, Apple Music, which favors a simpler and cleaner look-and-feel. Its recent changes have tried to address this problem by making its various parts and pages easier to use.
Spotify says the updated home screen will roll out starting today to all global users with at least 30 days of listening history.
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YouTube Music is launching three new personalized playlists
YouTube Music is preparing to better challenge Spotify and others with the launch of three new personalized playlists — Discover Mix, New Release Mix and Your Mix — said YouTube Chief Product Officer Neal Mohan in an onstage interview this morning at TechCrunch Disrupt SF 2019.
Discover Mix, YouTube Music’s version of Spotify’s Discover Weekly, had already been spotted in the wild back in September. But it wasn’t yet broadly available. The other two hadn’t yet launched.
“Our YouTube Music app has been out now for a couple of years, we’ve launched the YouTube Premium service and the app and now 71 different countries,” noted Mohan. “And as we’ve rolled it out, we’ve gotten lots of feedback from our users about what they’d love to see,” he continued. “And one of the things that they tell us repeatedly is, they love the fact that, through a combination of things like machine learning and human beings that are music lovers, we put all this great music in front of our users in the YouTube Music app,” he said.
According to Mohan, the Discover Mix will focus on helping users uncover new artists and music they might like, including tracks from artists you’ve never listened to before as well as lesser-known tracks from artists you already love.
The playlist takes advantage of your historical listening data on YouTube Music and on YouTube, he said.
New Release Mix, meanwhile, is YouTube Music’s version of Spotify’s Your Release Radar, and features the most recent release from your favorite artists.
Finally, Your Mix is a playlist that combines the music you love with songs you haven’t heard yet but will probably like, based on your listening habits.
The mixes will be updated weekly, and will be made available to all users worldwide, where they’ll be found on the “Mixed for You” shelf on the home screen, or by searching in the app.
All three will launch sometime later this month, but YouTube doesn’t have an exact date.
The additions arrive at a time when Google is preparing to transition its Google Play Music users over to YouTube Music, which makes it a much bigger threat to existing music streaming services, including Spotify, Apple Music, Amazon Music, Pandora and others.
While YouTube Music hasn’t yet replaced Play Music entirely or shut down the older app, it did just make YouTube Music the default music app that ships with new Android devices, instead of Google Play Music.
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Pandora puts its personalization powers to work in a revamped app
Pandora is doubling down on personalization and revamping its app in order to better compete with rivals like Spotify and Apple Music. Today, the company is introducing a new mobile experience that includes a dedicated “For You” tab where a continually updated feed of content is presented to users, including both music and podcast recommendations (and more). This content is personalized to the individual, based on factors like the day of the week, the time of day and Pandora’s predictions about your mood, among other things.
The new personalized feed will also help the company to better showcase more of its exclusive content — like its music-and-podcast combos, called “Pandora Stories,” for example. Or the dozens of SiriusXM talk shows that became Pandora podcasts following its acquisition.
“Our listeners have told us that they love the utility of Pandora — it’s drop-dead easy, it works, it knows me, it’s really simple,” explains Pandora’s Chief Product Officer Chris Phillips. “But what they haven’t been able to understand and have easy enough access to is all the content and programming that we have available on Pandora — the new content, new programming and the unique content that you can’t get other places,” he says.
The For You tab aims to change that by turning Pandora’s personalization capabilities onto its broader catalog and exclusives, then crafting a scrollable feed with dozens of ways to listen.
Here, you’ll be able to tap into Pandora Modes, for example, which is a new way to listen to Pandora Stations. The feature was previously available on the web, and has now come to mobile for the first time with today’s launch.
Pandora Modes let you toggle between ways to customize your stations. You can opt for modes that will tweak the station to play things like the most popular songs (“crowd faves”), the deep cuts, new releases, artist-only tracks and more. You also can opt for a “discovery” mode to have Pandora introduce you to new artists you may like, as related to the station in question.
Another section in the For You tab lets you browse by categories, including genre, new music, podcasts, moods, playlists, decades and trending.
The “Moods & Activities” section, meanwhile, will present collections of music based on current trends — for example, one of the available “moods” is “fall,” and another could be “rainy day,” matched up with the day’s weather. You also can dig into this section for moods to match your activity, like workout, gaming, studying, family time and more.
As you scroll down the For You page, you’ll come across your podcast recommendations and personalized playlists. And Pandora can create some 80 different versions of the latter, which include playlists by moods, activities, genres and more, all powered by its Music Genome.
Plus, the combined Pandora and SiriusXM editorial team of around 25 creates hundreds of human-curated playlists, too.
In total, there are some 35 different modules in Pandora’s new For You feed, some of which are shown to every user while others appear dynamically based on time of day and day of week. Its suggestions will also be tailored to your own likes and interests, thanks to your own listening behavior and explicit signals, like thumbs up and thumbs down.
That means your For You tab will be unique to you, and you can later be targeted with specific promotions — like the content to emerge from that deal between SiriusXM/Pandora and Drake, for example, if relevant to your interests. (Hey, it’s better than that time when Spotify put Drake’s face on every playlist.)
Despite the personalization, the feed will still include some insights powered by the larger Pandora population, so you can see what’s popular and trending more broadly across the service.
In time, Pandora plans to roll out even more modules to build out the experience further.
“100 billion thumbs are what’s powering all this,” adds Phillips, speaking of Pandora’s recent milestone, which measured the number of thumbs up and down clicks from users. Until now, he says, Pandora “hadn’t really brought together the community…and the power of our personalization, but not just for stations — for all the playlists, albums, songs and artists,” Phillips continues. “And then the idea that we lay on top of all of this…the idea of what time of day it is, and what might be interesting based on what we predict your mood is right now,” he says.
The “For You” tab and other features are arriving today on Pandora for iOS and Android.
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